4 research outputs found
A Business Intelligence Framework to Provide Performance Management through a Holistic Data Mining View
Traditional views of business intelligence have mainly focused on the physical and human aspects of the organization. This paper tries to show that a new information view of business activities can make a platform for developing business intelligence and support performance management. To do that, the paper proposes a new framework that can be used to provide high level of business intelligence for performance management usage. The framework introduces a hierarchy of performance influencers and a new methodology for managing them. The new methodology introduces a holistic view towards data mining concepts. The framework can be served as a blueprint for the companies which use any of ecommerce business models
EBusiness analytics framework (EBAF) - to enable SMEs to gain business intelligence for competitive advantage
Recent technological advances have resulted in increasingly larger databases. The fast and efficient
useful analysis and interpretation of this data to improve business intelligence is critical to the
success of all organisations. This thesis presents a new framework that utilises a new multilayer
mining theory and is based on business intelligence methods, data mining techniques, online
analytical processing (OLAP) and online transactional processing (OLTP). Existing decision making
modelling approaches for executive information systems have three main shortcomings and
limitations to different degrees: a) problems in accessing new types and new structures of data
sources; b) failing to provide organizational insight and panorama; and c) generating excessive
amount of trivial information.
The hypothesis of this research is that a new proactive Multidimensional Multilayer Mining
Management Model (5M) framework which is proposed in this thesis will overcome the
shortcomings listed above. The 5M framework is made up of 6 components: (a) multilayer mining
structures; (b) measurable objectives conversion models; (c) operational transaction databases; d)
object-model data marts; (e) data cubes and (f) core analysis engine which analyse the
multidimensional cubes, multilayer mining structures and the enterprise key performance indicators.
The 5M framework was evaluated by developing an implementation of an instance of the framework
called the Ebusiness Analytical Framework (EBAF). The 5M framework and the subsequent EBAF
framework were built by carrying out action research and a case study in an ebusiness company
where it was subject to implementation, reflection, adaptation and improvement in order to fulfil
the requirements of the hypothesis and that of a real business. EBAF implemented all 6 components
of the 5M framework using the Visual Studio environment and using various algorithms, tools and
programming languages. The programming languages used included MDX, DMX, SQL, VB.Net and C#.
Further empirical case studies can be carried out to evaluate the effectiveness and efficiency of the
5M framework